Adyasha Rath
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View article: Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features
Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features Open
Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG…
View article: Winograd Transform-Based Fast Detection of Heart Disease Using ECG Signals and Chest X-Ray Images
Winograd Transform-Based Fast Detection of Heart Disease Using ECG Signals and Chest X-Ray Images Open
In resource-constrained environments, efficient feature extraction is crucial for applications in classification and prediction tasks. This study investigates a fast, DFT-based, one-dimensional Winograd Transform (WT) to extract convolutio…
View article: Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers
Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers Open
Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CN…
View article: Enhanced Detection of Epileptic Seizure Using Hybrid Framework of Slantlet Transform and Spiking Neural Network Model
Enhanced Detection of Epileptic Seizure Using Hybrid Framework of Slantlet Transform and Spiking Neural Network Model Open
Epileptic seizures are sudden disturbances in the brain’s electrical activity, disrupting normal function. Early detection, accurate diagnosis, and timely treatment are essential to manage and reduce seizure frequency. This paper presents …
View article: A comparative ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer detection
A comparative ensemble approach of deep learning models for binary and multiclass classification of histopathological images for breast cancer detection Open
Breast cancer (BC) is the most frequently occurring cancer in women after lung cancer. There are different stages of breast cancer. Among them, Invasive ductal BC causes the maximum number of deaths in women. Different radio imaging techni…
View article: Breast cancer relapse disease prediction improvements with ensemble learning approaches
Breast cancer relapse disease prediction improvements with ensemble learning approaches Open
Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substant…
View article: A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique
A Novel Approach to Enhance Software Defect Prediction using An Improved Grey Wolf Optimization based Extreme Learning Machine Technique Open
In software development and testing, detecting and mitigating faults are paramount to prevent potential issues from escalating and disrupting the development and testing processes. The proposed method can also improve the prediction of var…
View article: An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection
An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection Open
A world of healthcare possibilities has been opened with the development of the Internet of Medical Things and related machine learning, deep learning, and artificial intelligence approaches. It has a broad range of uses: when linked to th…
View article: Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM
Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM Open
Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and the…
View article: Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection
Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection Open
Thyroid cancer is a life-threatening condition that arises from the cells of the thyroid gland located in the neck’s frontal region just below the adam’s apple. While it is not as prevalent as other types of cancer, it ranks prominently am…
View article: A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms
A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms Open
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized…
View article: Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique
Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique Open
The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS),…